CN117593168B - Ice lake burst risk assessment system and method - Google Patents
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Abstract
The invention discloses a system and a method for evaluating the risk of a burst of a ice lake, which particularly relate to the technical field of ice lake data analysis and are used for solving the problem of inaccuracy of ice lake burst risk evaluation.
Description
Technical Field
The invention relates to the technical field of ice lake data analysis, in particular to an ice lake breaking risk assessment system and an ice lake breaking risk assessment method.
Background
The burst of the glacier refers to the phenomenon that the water level of the glacier is increased due to the melting of glaciers or other factors, and finally the edge of the glacier dam or the edge of the glacier is broken, so that a large amount of water sources are released to enter a downstream area, and flood disasters are caused. This process typically involves multiple links such as breaking of the ice dams in the ice lakes, dropping of ice cubes, and large-scale flood discharge of the lake water.
The prior art has the following defects:
The accuracy of ice lake burst evaluation is limited by data, and can cause insufficient comprehensive evaluation results due to the fact that remote sensing data are lost or updated untimely, the quality difference of remote sensing data acquired by different satellites and sensors is large, the difference in spectral resolution, spatial resolution and the like exists, the consistency of the evaluation results is affected, the prediction accuracy of an ice lake burst evaluation model is further affected, in different areas and in different seasons, the ice lake burst factors are caused to be different, the data affecting the ice lake are difficult to accurately acquire in time, the evaluation of ice lake burst is further affected, corresponding strategy regulation cannot be timely conducted, and more losses are caused when burst risks are generated.
Disclosure of Invention
In order to overcome the defects of the prior art, the embodiment of the invention provides a system and a method for evaluating the risk of ice lake burst, which are characterized in that remote sensing images of remote sensing areas are acquired through remote sensing satellites, the remote sensing images are analyzed to obtain an ice lake data set with space-time characteristics, then the ice lake data set is subjected to extraction of risk evaluation indexes, the risk evaluation indexes conforming to the actual conditions of a research area are determined, the risk evaluation indexes are analyzed based on Pelson correlation coefficients, the impact indexes of the ice lake burst are classified, an ice lake burst risk evaluation model is constructed by adopting a hierarchical analysis method, the analysis is performed, the situation of the ice lake burst risk evaluation model is determined, and further the evaluation and regulation of the ice lake burst risk are accurate, so that the problems in the background art are solved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
An ice lake burst risk assessment method comprises the following steps:
Remote sensing area monitoring is carried out through a remote sensing satellite, a remote sensing image obtained through monitoring is interpreted, the spatial attribute of the ice lake is obtained, and an ice lake data set with space-time characteristics is obtained;
Extracting a risk assessment index from the ice lake data set monitored by remote sensing, and determining the risk assessment index which accords with the actual situation of a research area;
performing band fusion on the acquired and downloaded remote sensing images to complete establishment of a lagoon database, analyzing risk assessment indexes based on pearson correlation coefficients, and classifying impact indexes of lagoon breach;
Constructing an ice lake burst risk evaluation model by adopting an analytic hierarchy process, analyzing various data generated in the constructed model, and determining the situation of the ice lake burst risk evaluation model;
And (3) according to the situation analysis of the ice lake burst risk evaluation model, carrying out evaluation and control on the ice lake burst risk.
In a preferred embodiment, remote sensing region monitoring is performed through a remote sensing satellite, and the remote sensing image obtained through monitoring is interpreted and the spatial attribute of the ice lake is obtained, so that an ice lake data set with space-time characteristics is obtained, and the specific process is as follows:
Acquiring spectrum information and space-time characteristic information acquired by remote sensing satellites under different time, seasons, weather, satellite heights and resolutions;
And (3) obtaining the spatial attribute of the ice lake through remote sensing image interpretation, determining the shape, size and position attribute of the ice lake according to the spatial attribute information of the remote sensing image, and forming an ice lake data set with space-time characteristics.
In a preferred embodiment, the establishment of the ice lake database is completed by performing band fusion on the collected and downloaded remote sensing images, and the specific process is as follows:
Performing band fusion on the acquired and downloaded remote sensing images, ensuring that band data for extracting NDWI are effective, and checking data attribute fields and projection coordinates on vector files required by the ice lake;
Using an extraction tool to obtain the NDWI value of the ground object in the region of interest, adjusting a threshold value according to the NDWI histogram and the NDWI raster data, determining a minimum value graph unit, and generating an ice lake boundary vector file;
And (3) combining the ice lake data set and the high-resolution image to perform inspection and manual correction on the generated ice lake data, and performing geometric inspection and topology inspection on the data to complete establishment of an ice lake database.
In a preferred embodiment, the risk assessment index is analyzed based on pearson correlation coefficients, and the impact index of the ice lake breach is classified as follows:
Defining a breaking dangerous index and disaster occurrence possibility and calculating an influence value;
calculating influence values of different categories under each influence index of ice lake burst according to the burst certainty coefficient;
The influence values of the same category are summarized as the total influence index, and the influence index is classified into a high risk index and a low risk index.
In a preferred embodiment, an ice lake burst risk evaluation model is constructed by adopting an analytic hierarchy process, and various data generated in the constructed model are analyzed, wherein the specific process is as follows:
determining various indexes influencing the burst risk of the ice lake, wherein the indexes are decomposed into specific sub-indexes to form a hierarchical structure;
constructing a judgment matrix, constructing a judgment matrix for each pair of indexes, and calculating the maximum characteristic value of the judgment matrix and the characteristic vector corresponding to the maximum characteristic value;
Calculating the weight of each index to the previous index by decomposing the characteristic value of the judgment matrix, and calculating the weight vector of each index layer by a hierarchical structure
The weight is applied to grading of the evaluation index, a final ice lake burst risk evaluation result is calculated according to the hierarchical structure, and evaluation result data are input into a model for training analysis.
In a preferred embodiment, according to the situation analysis of the ice lake burst risk evaluation model, the evaluation and control of the ice lake burst risk are performed, and the specific process is as follows:
Analyzing various data generated in the model construction process to obtain drawing determination information and index weight influence information in the model construction process;
the drawing determining information comprises an image drawing error index, and the index weight influence information comprises a breaking index weight floating index;
calculating the obtained image drawing error index and the burst index weight floating index to obtain a risk judgment coefficient;
the image drawing error index and the breaking index weight floating index are in direct proportion to the risk judging coefficient;
and comparing the generated risk judgment coefficient with an accurate evaluation threshold value.
In a preferred embodiment, the risk assessment coefficients generated are compared to an accurate assessment threshold, as follows:
comparing the risk judgment coefficient with an accurate evaluation threshold value;
If the risk judgment coefficient is greater than or equal to the accurate evaluation threshold value, generating a iced lake evaluation abnormal signal, and performing regulation and control management;
If the risk judgment coefficient is smaller than the accurate evaluation threshold value, generating a stable ice lake risk evaluation signal, and keeping normal management.
In a preferred embodiment, if the risk determination coefficient is greater than or equal to the accurate evaluation threshold, generating a iced lake evaluation abnormal signal, and performing regulation management refers to re-examining input data including remote sensing images and terrain data, performing data cleaning and repair, checking and adjusting parameters of the iced lake breaking risk evaluation model, and adjusting weight and threshold parameters according to characteristics of the abnormal signal.
An ice lake burst risk assessment system for the ice lake burst risk assessment method, comprising:
The remote sensing image information acquisition module is used for acquiring remote sensing images obtained by remote sensing area monitoring of remote sensing satellites;
The influence index analysis module is used for extracting the risk assessment index of the ice lake data set monitored by remote sensing, determining the risk assessment index which accords with the actual situation of a research area, performing band fusion on the collected and downloaded remote sensing image to complete the establishment of the ice lake database, analyzing the risk assessment index based on the pearson correlation coefficient, and classifying the influence index of the ice lake break;
The management control module is used for constructing a iced lake burst risk evaluation model, analyzing various data generated in the constructed model and determining the condition of the iced lake burst risk evaluation model;
The adjusting module is used for analyzing the situation of the ice lake burst risk evaluation model and evaluating and controlling the ice lake burst risk.
The invention has the technical effects and advantages that:
The method comprises the steps of collecting remote sensing images of a remote sensing area through a remote sensing satellite, analyzing to obtain a icelake data set with space-time characteristics, extracting risk assessment indexes of the icelake data set, determining the risk assessment indexes which accord with the actual situation of a research area, analyzing the risk assessment indexes based on Pelson correlation coefficients, classifying the impact indexes of icelake breaking, constructing a icelake breaking risk assessment model through an analytic hierarchy process, analyzing various data generated in the constructed model, and determining the situation of the icelake breaking risk assessment model, so that the evaluation and regulation of the icelake breaking risk are carried out, and the accuracy of the icelake breaking risk evaluation is improved.
Drawings
Fig. 1 is a flowchart of a method for evaluating a burst risk of a lagoon according to the present invention.
Fig. 2 is a schematic structural diagram of a system for evaluating burst risk of a lagoon according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, a method for evaluating a burst risk of a lagoon, the method comprising:
the method comprises the steps that the ice lakes are distributed in high-altitude glacier landforms, remote sensing area monitoring is carried out through remote sensing satellites, such as Sentinel series satellites and high-resolution series satellites, the spatial attributes of the ice lakes are interpreted and acquired through remote sensing images, and due to the diversity of remote sensing data acquisition, factors such as different time, seasons, weather, satellite heights and resolution are included, the spectral information and the space-time characteristics of the ice lakes are different, and when the ice lake remote sensing monitoring is carried out, the ice lakes are firstly required to be defined and extracted. And acquiring the spatial attribute of the ice lake through remote sensing image interpretation, determining the shape, size, position and other attributes of the ice lake by means of the spatial information of the remote sensing image, and finally forming an ice lake data set with space-time characteristics.
The process involves overcoming various variables of remote sensing data acquisition, analyzing images at different time points, processing seasonal changes and differences of illumination conditions to ensure accurate and reliable ice lake information, and analyzing the distribution and evolution trend of the ice lake and the potential influence on the surrounding environment by establishing and analyzing an ice lake data set;
according to the remote sensing data standardization, the risk assessment of the ice lake is carried out, and a large number of indexes collected in aspects of lake bodies, dam bodies, glaciers, terrains and the like are analyzed, wherein the ice lake characteristics comprise characteristics of ice lake areas, water quantities and the like, and the ice lake characteristics cover the scale and water resource quantity of the ice lake; the characteristics of the ice lake dam body comprise characteristics of dam body material composition, dam body gradient, dam body aspect ratio, height above the lake surface of the dam body, water outlet width and the like, and the characteristics are related to the ice dams at the edge of the ice lake; the characteristics of the trailing edge parent glacier comprise characteristics of glacier gradient, glacier fracture condition, the distance between the glacier lake and the glacier and the like;
The peripheral physical characteristics of the glacier include avalanche or characteristics of the probability of rock collapse entering the lake, the probability of landslide entering the lake and the like, and the glacier breaking danger can be more comprehensively estimated by considering the change of peripheral topography and possible physical processes, and the characteristics are obtained according to historical record data;
Other factors include seismic and extreme climate changes, which may have a significant impact on the stability and risk of the ice lake, and which also need to be considered in the evaluation, derived from historical data;
the method comprises the steps of extracting risk assessment indexes from ice lake data monitored by remote sensing, wherein ice lake breaking assessment has regional constraint, and is separated from actual condition selection indexes of a research area, so that one side of an assessment result is easy to appear, and therefore, when the risk assessment indexes are selected, historical ice lake breaking events of the research area need to be widely referred to, and the risk assessment indexes which accord with the actual condition of the research area are determined;
the method comprises the steps of extracting ice lake boundaries according to the spatial position, the shape and the size of an ice lake and spectral characteristics, checking and correcting the results to form ice lake data, analyzing the ice lake water data, detecting the ice lake data by using a Normalized Water Index (NWI), wherein the normalized water index is a remote sensing index for detecting and quantifying surface water, and one common normalized water index is a Normalized Difference Water Index (NDWI) expressed as follows: Where NIR is the reflection in the near infrared band, SWIR is the reflection in the short wave infrared band, and NDWI is typically in the range of-1 to 1, indicating that the body of water has a high reflectance in the near infrared band and a lower reflectance in the short wave infrared band. Thus, the NDWI value for the water body region is higher, while the NDWI value for the non-water body region is lower. In general, thresholding may be used to binarize the NDWI image to more clearly distinguish between water and non-water regions.
It should be noted that the specific normalized water index may vary from one study requirement to another and from one data source to another, and in some cases may be adjusted by using combinations of other bands or correction factors.
Performing band fusion on the acquired and downloaded remote sensing images, ensuring that band data for extracting NDWI are effective, and checking data attribute fields and projection coordinates on vector files required by the ice lake;
Using an extraction tool to obtain the NDWI value of the ground object in the region of interest, and performing threshold adjustment according to the NDWI histogram and the NDWI raster data;
determining a minimum value graph unit and generating an ice lake boundary vector file;
And (3) combining the ice lake data set and the high-resolution image to perform inspection and manual correction on the generated ice lake data, and performing geometric inspection and topology inspection on the data to complete establishment of an ice lake database.
In the deterministic coefficient method, the risk index is analyzed based on the pearson correlation coefficient, the deterministic coefficient generally measures the linear relationship between two variables, defines the breaking risk index X and the disaster occurrence probability Y, and calculates the impact value ND as follows: In the above, the ratio of/> Is the covariance between the risk index X and the disaster occurrence probability Y,/>And/>The standard deviation of the hazard indexes X and the hazard occurrence probability Y are respectively used for quantifying the relation between the hazard indexes and the hazard occurrence probability Y so as to classify the hazard indexes, wherein the standard deviation of the hazard indexes is used for measuring the variation degree of the hazard indexes, the standard deviation of the hazard occurrence probability is used for measuring the variation degree of the hazard indexes, the hazard occurrence probability can be a discrete variable, and the hazard indexes can be various environmental factors related to the collapse, such as geology, topography and the like. The value of this function ranges from-1 to 1, negative values indicate a negative correlation, positive values indicate a positive correlation, and 0 indicates no correlation.
It is noted that the deterministic coefficient method assumes that disaster datasets have occurred that are valid, and that future disaster likelihoods can be estimated by statistical relationships between these datasets and risk indicators.
And (3) calculating ND values of different types of each influence index of the ice lake burst according to the burst certainty coefficient, so as to evaluate the index, wherein for example, the ND value is positive, which indicates that the ice volume has a strong positive correlation with the burst disaster. The evaluation index is a high-risk index, the ND value is negative, the ice volume and the breaking disaster are in negative correlation, and the evaluation index is a low-risk index.
When the ice lake burst risk evaluation model is constructed, an analytic hierarchy process (AHP method) is adopted, and the following specific processes and steps of the analytic hierarchy process are as follows:
Various indicators are determined that affect the risk of ice lake breach, including geology, topography, meteorological conditions, ice lake characteristics, and the like. Each index can be further decomposed into specific sub-indexes to form a hierarchical structure;
carrying out quartile grading on each index, and dividing the index into different grades so as to facilitate subsequent quantitative evaluation;
Constructing a judgment matrix, wherein for each pair of indexes, a judgment matrix is constructed, the matrix reflects the relative importance between the two indexes, and expert judgment or questionnaire investigation is needed to acquire weight information;
Calculating the maximum eigenvalue of the judgment matrix A Calculating a feature vector corresponding to the maximum feature value;
Calculating the weight of each index relative to the index of the previous stage by performing eigenvalue decomposition on the judgment matrix by using an AHP method, and calculating layer by a hierarchical structure to finally obtain the weight vector of each index;
consistency test is carried out to ensure that the data judged or input by an expert are consistent, if the data are inconsistent, the judgment matrix is required to be readjusted, the weight is applied to the grading of the evaluation indexes, and the final ice lake burst risk evaluation result is calculated according to the hierarchical structure, wherein the calculation formula of the consistency indexes is as follows: N represents the order of the judgment matrix, the corresponding random consistency ratio RI is obtained according to the order n in a table lookup mode, and the consistency ratio is calculated: /(I) When CR is smaller than or equal to 0.1, the judgment matrix passes consistency test, the feature vector is normalized to obtain a weight vector, otherwise, proper adjustment is carried out on the constructed matrix until the final result meets the final requirement of consistency in the representative value range.
Analyzing various data generated in the model construction process to determine the situation of the ice lake breaking risk evaluation model, namely, drawing determination information and index weight influence information in the model construction process are obtained;
the drawing determining information comprises an image drawing error index and is calibrated to be YXZ, and the index weight influence information comprises a breaking index weight floating index and is calibrated to be KJZ;
The image drawing error index in the drawing determination information is an index for describing the accuracy and precision of a remote sensing image, in the construction process of an ice lake burst risk evaluation model, linear errors changing along with the perimeter of the ice lake exist in ice lake drawing, the real boundary of the ice lake is different from grid pixels, and the drawing determination information relates to the processing and interpretation of the remote sensing image, so that the image drawing error index can be used for evaluating the drawing accuracy of the model and measuring the alignment degree of the remote sensing image and a geographic coordinate system, the smaller the image drawing error index is, the more accurate the correspondence between the image and the actual geographic position is, and the image drawing error index can play a role in the following aspects:
Geographic accuracy assessment: the image drawing error index is mainly used for measuring the alignment degree, namely registration error, of the remote sensing image and the geographic coordinate system, and the degree of deviation between the ground object in the remote sensing image and the actual geographic position can be known by evaluating the index, so that the smaller the image drawing error index is, the higher the geographic accuracy of drawing is;
Model input data quality control: the evaluation of the drawing error index can also be used as the basis of the quality control of the remote sensing data. If the registration error of the remote sensing image is large, the data needs to be corrected or other more accurate images are used;
reliability evaluation of drawing results: the drawing error directly influences the reliability of the model output, if the image drawing error is larger, the collapse risk evaluation result of the model output is possibly inaccurate, and therefore, the reliability of the drawing result can be evaluated by examining the drawing error index.
The image drawing error index is obtained by the following steps: acquiring the perimeter ZC of the ice lake and the pixel value SK of the remote sensing image, and acquiring coordinate data of an ith sample point in reference data (geographic coordinate system)Acquiring coordinate data/>, corresponding to i samples, in the registered remote sensing image dataCalculating registration deviation of sample points,/>N is a positive integer, the field investigation data or the high-resolution image containing the ground object category is obtained, an confusion matrix is constructed by using the interpretation result and the field investigation data or the high-resolution image, and an image classification determination value is calculated: /(I)N is the total pixel number, p and q are each element of the confusion matrix, the image drawing error index is obtained through calculation, and the calculation expression is:。
it should be noted that, the pixel value of the remote sensing image represents the pixel size, the registration deviation calculates the offset of each sample point in the horizontal and vertical directions, then averages the offsets of all sample points and takes the square root, and the result is an average square root error, which is used for representing the overall registration deviation, and the smaller the value, the more accurate the alignment of the remote sensing image with the geographic coordinate system; and constructing a confusion matrix by using the interpretation result and the field investigation data or the high-resolution image, wherein the confusion matrix is a two-dimensional table, which contains the cross statistics between the model classification result and the actual ground condition, performing ground feature interpretation by using a remote sensing image, distributing pixels in the image into different ground feature categories, and completing the classification by a supervised or unsupervised classification method, wherein the total number of pixels is the total number of classified pixels.
The impact of the index weight on the determined index weight floating index in the information indicates the change or floating condition of the weight of the determined index under different conditions, which can help to evaluate the sensitivity and stability of the model to specific indexes under different conditions, and is used for analyzing the impact of the state of the ice lake indexes in different periods on the possible determination of the determined index, wherein the impact of the determined index weight floating index has the following effects:
Improving decision support capability: in the decision making process, the capability of improving decision support is facilitated by knowing the floating condition of the breaking index weight, and a decision maker can more comprehensively consider the output of the model under different situations to make a more robust decision, so that the situation of breaking transmission is reduced or the loss caused by breaking is reduced;
The changing environment should be: when the external environment changes such as climate change and geological condition change are faced, the response of the model to the changes can be better understood by analyzing the floating condition of the breaking index weight, and the corresponding adaptability strategy can be formulated.
The method for obtaining the burst index weight floating index is as follows:
Obtaining the certainty factor to obtain the grading data of the burst dangerous index and the index value data of each element of the ice lake, establishing an influence value set for the influence value ND, Acquiring weight values of all influence factors obtained according to an analytic hierarchy process and establishing a weight value set, wherein the weight values are obtained by using a method of hierarchical analysisM is a positive integer, a weight value obtained in the analytic hierarchy process is obtained, and multiplication and addition calculation are carried out on the weight value and an influence value, so that a dangerous initial estimated value is obtained: /(I)Establishing a dangerous initial value set, and calculating the average value/>, of the dangerous initial value setCalculating a burst index weight floating index, wherein the calculation expression is as follows: /(I)。
The obtained image drawing error index YXZ and the collapsed index weight floating index KJZ are comprehensively calculated to obtain a risk judgment coefficient, and the expression is as follows: In the above, the ratio of/> Is a risk determination coefficient,/>、/>The preset scale coefficients of the image drawing error index YXZ and the collapse index weight floating index KJZ are determined by/>、/>Are all greater than 0.
It should be noted that, the size of the preset scaling factor is a specific numerical value obtained by quantizing each parameter, and in order to facilitate the subsequent comparison, the size of the scaling factor depends on the number of sample data and the person skilled in the art to initially set a corresponding preset scaling factor for each group of sample data; and the method is not unique, and only the proportional relation between the parameter and the quantized numerical value is not influenced, for example, the proportional relation between the breaking index weight floating index and the risk judging coefficient is determined.
The larger the image drawing error index is, the larger the burst index weight floating index is, namely the larger the representation value of the risk judgment coefficient is, which shows that the degree of correspondence between the geometric position of the remote sensing image and the actual geographic position is lower, namely the drawing accuracy and precision are poorer, the input data accuracy of the ice lake burst model is affected, the uncertainty of the model output result is increased, the potential risk is increased, namely the output result of the model is changed more when facing different conditions, and meanwhile, the inaccuracy of the input data is also increased, so that the risk assessment of ice lake burst is easier to be adversely affected;
The smaller the image drawing error index and the smaller the breaking index weight floating index are, namely the smaller the appearance value of the risk judging coefficient is, the lower the potential risk is, namely the output result of the model changes little when facing different conditions, and meanwhile, the accuracy of input data is also higher, so that the risk assessment of the breaking of the ice lake is less influenced, and the accuracy of the risk assessment of the breaking of the ice lake is improved.
Comparing the generated risk judgment coefficient with an accurate evaluation threshold value, generating different signals, and carrying out ice lake analysis management according to the generated signals;
after acquiring the risk judgment coefficient, comparing the risk judgment coefficient with an accurate evaluation threshold value;
If the risk judgment coefficient is larger than or equal to the accurate evaluation threshold value, generating a iced lake evaluation abnormal signal, wherein the larger risk judgment coefficient means that the model output result is larger in change under different conditions, and meanwhile, the accuracy of input data is poorer, so that potential risk is increased, the accurate evaluation of the iced lake breaking potential risk is influenced, and a decision maker needs to be cautious when preparing management and prevention strategies;
If the risk judgment coefficient is smaller than the accurate evaluation threshold value, generating a stability signal of the ice lake risk evaluation, and indicating that the result output by the model is relatively stable. This is because the model responds more smoothly to changes in different conditions, and the accuracy of the input data is higher, which increases the confidence in accurately assessing the potential risk of ice lake breach, and the decision maker can take corresponding actions more on a basis.
When generating the ice lake assessment anomaly signal, a series of steps need to be taken to deal with this situation to improve the accuracy and reliability of the model, managed as follows:
And re-examining input data, including remote sensing images, topographic data and the like, so as to ensure the accuracy and the integrity of the data, correct possible data errors or deletions, clean and repair the data, check and adjust parameters of the model to ensure the model to adapt to changes under different conditions, and adjust the parameters of the model, such as weight, threshold and the like according to the characteristics of abnormal signals, so that the model operates normally.
When a stable signal of the risk assessment of the ice lake is generated, the model is relatively stable in output, the accuracy of input data is high, and the management is carried out according to the following steps:
and comprehensively analyzing the model output result, confirming the consistency of the model output result under different conditions, maintaining the existing monitoring mechanism, ensuring that new observation data and input data are timely acquired, and timely giving early warning to related personnel when the iced lake breaks the risk.
In the present embodiment, the threshold values are set according to actual conditions, and are not fixed values, and thus, excessive analysis is not performed.
The method comprises the steps of collecting remote sensing images of a remote sensing area through a remote sensing satellite, analyzing to obtain a icelake data set with space-time characteristics, extracting risk assessment indexes of the icelake data set, determining the risk assessment indexes which accord with the actual situation of a research area, analyzing the risk assessment indexes based on Pelson correlation coefficients, classifying the impact indexes of icelake breaking, constructing a icelake breaking risk assessment model through an analytic hierarchy process, analyzing various data generated in the constructed model, and determining the situation of the icelake breaking risk assessment model, so that the evaluation and regulation of the icelake breaking risk are carried out, and the accuracy of the icelake breaking risk evaluation is improved.
Embodiment 2 is an embodiment of a system of embodiment 1, configured to implement the method for evaluating a burst risk of a iced lake described in embodiment 1, as shown in fig. 2, and specifically includes:
The remote sensing image information acquisition module is used for acquiring remote sensing images obtained by remote sensing area monitoring of remote sensing satellites;
The influence index analysis module is used for extracting the risk assessment index of the ice lake data set monitored by remote sensing, determining the risk assessment index which accords with the actual situation of a research area, performing band fusion on the collected and downloaded remote sensing image to complete the establishment of the ice lake database, analyzing the risk assessment index based on the pearson correlation coefficient, and classifying the influence index of the ice lake break;
The management control module is used for constructing a iced lake burst risk evaluation model, analyzing various data generated in the constructed model and determining the condition of the iced lake burst risk evaluation model;
The adjusting module is used for analyzing the situation of the ice lake burst risk evaluation model and evaluating and controlling the ice lake burst risk.
The above formulas are all formulas for removing dimensions and taking numerical calculation, and specific dimensions can be removed by adopting various means such as standardization, and the like, which are not described in detail herein, wherein the formulas are formulas for acquiring a large amount of data and performing software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, ATA hard disk, tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state ATA hard disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. The ice lake burst risk assessment method is characterized by comprising the following steps of:
Remote sensing area monitoring is carried out through a remote sensing satellite, a remote sensing image obtained through monitoring is interpreted, the spatial attribute of the ice lake is obtained, and an ice lake data set with space-time characteristics is obtained;
Extracting a risk assessment index from the ice lake data set monitored by remote sensing, and determining the risk assessment index which accords with the actual situation of a research area;
performing band fusion on the acquired and downloaded remote sensing images to complete establishment of a lagoon database, analyzing risk assessment indexes based on pearson correlation coefficients, and classifying impact indexes of lagoon breach;
Constructing an ice lake burst risk evaluation model by adopting an analytic hierarchy process, analyzing various data generated in the constructed model, and determining the situation of the ice lake burst risk evaluation model;
according to the situation analysis of the ice lake burst risk evaluation model, the evaluation and control of the ice lake burst risk are carried out;
Remote sensing region monitoring is carried out through a remote sensing satellite, the remote sensing image obtained through monitoring is interpreted, the space attribute of the ice lake is obtained, and the ice lake data set with space-time characteristics is obtained, wherein the specific process is as follows:
Acquiring spectrum information and space-time characteristic information acquired by remote sensing satellites under different time, seasons, weather, satellite heights and resolutions;
The method comprises the steps of obtaining the spatial attribute of the ice lake through remote sensing image interpretation, determining the shape, size and position attribute of the ice lake according to the spatial attribute information of the remote sensing image, and forming an ice lake data set with space-time characteristics;
the establishment of the ice lake database is completed by carrying out band fusion on the collected and downloaded remote sensing images, and the specific process is as follows:
Performing band fusion on the acquired and downloaded remote sensing images, and checking data attribute fields and projection coordinates on vector files required by the ice lakes;
Using an extraction tool to obtain the NDWI value of the ground object in the region of interest, adjusting a threshold value according to the NDWI histogram and the NDWI raster data, determining a minimum value graph unit, and generating an ice lake boundary vector file;
The method comprises the steps of combining a glacial lake data set and a high-resolution image to perform inspection and manual correction on the generated glacial lake data, performing geometric inspection and topology inspection on the data, and completing establishment of a glacial lake database;
based on the pearson correlation coefficient analysis risk assessment index, the influence index of the ice lake break is classified, and the specific process is as follows:
Defining a breaking dangerous index and disaster occurrence possibility and calculating an influence value;
calculating influence values of different categories under each influence index of ice lake burst according to the burst certainty coefficient;
Summarizing the influence values of the same category as a total influence index, and classifying the influence index into a high-risk index and a low-risk index;
An ice lake burst risk evaluation model is constructed by adopting an analytic hierarchy process, and various data generated in the constructed model are analyzed, wherein the specific process is as follows:
determining various indexes influencing the burst risk of the ice lake, wherein the indexes are decomposed into specific sub-indexes to form a hierarchical structure;
constructing a judgment matrix, constructing a judgment matrix for each pair of indexes, and calculating the maximum characteristic value of the judgment matrix and the characteristic vector corresponding to the maximum characteristic value;
calculating the weight of each index to the previous level index by decomposing the characteristic value of the judgment matrix, and calculating the weight vector of each index layer by a hierarchical structure to finally obtain the weight vector of each index;
the weight is applied to grading of the evaluation index, a final ice lake burst risk evaluation result is calculated according to the hierarchical structure, and evaluation result data are input into a model for training analysis;
According to the situation analysis of the ice lake burst risk evaluation model, the evaluation and control of the ice lake burst risk are carried out, and the concrete process is as follows:
Analyzing various data generated in the model construction process to obtain drawing determination information and index weight influence information in the model construction process;
the drawing determining information comprises an image drawing error index, and the index weight influence information comprises a breaking index weight floating index;
calculating the obtained image drawing error index and the burst index weight floating index to obtain a risk judgment coefficient;
comparing the generated risk judgment coefficient with an accurate evaluation threshold value;
The method for obtaining the burst index weight floating index is as follows:
Obtaining the certainty factor to obtain the grading data of the burst dangerous index and the index value data of each element of the ice lake, establishing an influence value set for the influence value ND, Acquiring weight values of all influence factors obtained according to an analytic hierarchy process and establishing a weight value set, wherein the weight values are obtained by using a method of hierarchical analysisM is a positive integer, and the weight value obtained in the analytic hierarchy process is multiplied and added with the influence value to obtain a dangerous initial value: /(I)Establishing a dangerous initial value set, and calculating the average value/>, of the dangerous initial value setCalculating a burst index weight floating index, wherein the calculation expression is as follows: /(I)；
The image drawing error index is obtained by the following steps:
Acquiring the perimeter ZC of the ice lake and the pixel value SK of the remote sensing image, and acquiring coordinate data of an ith sample point in a geographic coordinate system Acquiring coordinate data/>, corresponding to i samples, in the registered remote sensing image dataCalculating registration deviation of sample points,/>N is a positive integer, the field investigation data or the high-resolution image containing the ground object category is obtained, an confusion matrix is constructed by using the interpretation result and the field investigation data or the high-resolution image, and an image classification determination value is calculated: /(I)N is the total pixel number, p and q are each element of the confusion matrix, the image drawing error index is obtained through calculation, and the calculation expression is: /(I)；
The obtained image drawing error index YXZ and the collapsed index weight floating index KJZ are comprehensively calculated to obtain a risk judgment coefficient, and the expression is as follows: In the above, the ratio of/> Is a risk determination coefficient,/>、/>The preset scale coefficients of the image drawing error index YXZ and the collapse index weight floating index KJZ are determined by/>、/>Are all greater than 0.
2. The method for evaluating the burst risk of a lagoon according to claim 1, wherein the method comprises the following steps: comparing the generated risk judgment coefficient with an accurate evaluation threshold value, wherein the specific process is as follows:
comparing the risk judgment coefficient with an accurate evaluation threshold value;
If the risk judgment coefficient is greater than or equal to the accurate evaluation threshold value, generating a iced lake evaluation abnormal signal, and performing regulation and control management;
If the risk judgment coefficient is smaller than the accurate evaluation threshold value, generating a stable ice lake risk evaluation signal, and keeping normal management.
3. The method for evaluating the burst risk of a lagoon according to claim 2, wherein the method comprises the following steps: if the risk judgment coefficient is greater than or equal to the accurate evaluation threshold, generating a glacier lake evaluation abnormal signal, and performing regulation and control management refers to re-examining input data, including remote sensing images and topographic data, performing data cleaning and repairing, checking and adjusting parameters of a glacier lake breaking risk evaluation model, and adjusting weight and threshold parameters according to characteristics of the abnormal signal.
4. A system for ice lake burst risk assessment for implementing the method for ice lake burst risk assessment of any one of claims 1-3, comprising:
The remote sensing image information acquisition module is used for acquiring remote sensing images obtained by remote sensing area monitoring of remote sensing satellites;
The influence index analysis module is used for extracting the risk assessment index of the ice lake data set monitored by remote sensing, determining the risk assessment index which accords with the actual situation of a research area, performing band fusion on the collected and downloaded remote sensing image to complete the establishment of the ice lake database, analyzing the risk assessment index based on the pearson correlation coefficient, and classifying the influence index of the ice lake break;
The management control module is used for constructing a iced lake burst risk evaluation model, analyzing various data generated in the constructed model and determining the condition of the iced lake burst risk evaluation model;
The adjusting module is used for analyzing the situation of the ice lake burst risk evaluation model and evaluating and controlling the ice lake burst risk.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108986413A (en) * | 2018-08-16 | 2018-12-11 | 长江水利委员会长江科学院 | A kind of glade disaster monitoring and method for early warning based on multi-source information data |
| CN111932567A (en) * | 2020-07-30 | 2020-11-13 | 中国科学院空天信息创新研究院 | Satellite image-based ice lake contour automatic extraction method |
| CN114373283A (en) * | 2022-03-22 | 2022-04-19 | 中国科学院、水利部成都山地灾害与环境研究所 | Early warning method for burst disaster of ice-disintegrating type tillite lake |
| CN114529823A (en) * | 2022-02-28 | 2022-05-24 | 中国地质科学院岩溶地质研究所 | Comprehensive remote sensing identification method and system for ice lake burst hidden danger |
| US11645900B1 (en) * | 2022-12-08 | 2023-05-09 | Institute Of Mountain Hazards And Environment, Cas | Early warning method for icefall-caused moraine lake outburst disasters |
-
2024
- 2024-01-18 CN CN202410071046.6A patent/CN117593168B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108986413A (en) * | 2018-08-16 | 2018-12-11 | 长江水利委员会长江科学院 | A kind of glade disaster monitoring and method for early warning based on multi-source information data |
| CN111932567A (en) * | 2020-07-30 | 2020-11-13 | 中国科学院空天信息创新研究院 | Satellite image-based ice lake contour automatic extraction method |
| CN114529823A (en) * | 2022-02-28 | 2022-05-24 | 中国地质科学院岩溶地质研究所 | Comprehensive remote sensing identification method and system for ice lake burst hidden danger |
| CN114373283A (en) * | 2022-03-22 | 2022-04-19 | 中国科学院、水利部成都山地灾害与环境研究所 | Early warning method for burst disaster of ice-disintegrating type tillite lake |
| US11645900B1 (en) * | 2022-12-08 | 2023-05-09 | Institute Of Mountain Hazards And Environment, Cas | Early warning method for icefall-caused moraine lake outburst disasters |
Non-Patent Citations (3)
| Title |
|---|
| 基于GIS的冰川湖溃决灾害风险评估——以聂拉木县城为例;裴希琛;《中国优秀硕士学位论文全文数据库》;20200215(第2期);15-37 * |
| 基于多源遥感数据的"三极"冰湖时空变化特征与气候响应研究;王辉;《中国优秀硕士学位论文全文数据库》;20210215(第2期);1, 48 * |
| 模糊综合评判法在冰湖溃决危险度划分中的应用――以西藏自治区洛扎县为例;黄静莉;王常明;王钢城;张成杰;;地球与环境;20051231;33(S1);109-114 * |
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